Using Ollama with Llama_index

How are they working with Settings, vector store, index, ....

If I switch to a new llm instance, Settings.llm is also required to change?

chat_engine = index.as_chat_engine(llm=xxxx, memory=xxx, ...)

Settings is just a global default. You can pass in to override locally, as your code sample above does

I have a question about vectorstore, as multiple models available in my program.

vectorstore = DuckDBVectorsStore.from_local(db_path)
index = VectorStoreIndex.from_vector_store(vectorstore)

The problem is that when I add documents to the DB, it also changes the data in the drive.

Supposing we use phi + DB + doc_A, then switch to llama + DB + doc_B. In my current program, the switching requires remove doc_A from DB and append doc_B to DB.

Is it possible to load and create multiple vectorstore, append/remove doc doesn't affect the DB in the drive?

I don't think I really understand 🤔

https://docs.llamaindex.ai/en/stable/module_guides/indexing/vector_store_index/
it is related to this one.

DuckDBVectorsStore.from_local(db_path)
db_path is the file path for pre-saved database.

vectorstore = DuckDBVectorsStore.from_local(db_path)
index = VectorStoreIndex.from_vector_store(vectorstore)
llama_chat_engine = index.as_chat_engine(llm=llama_lm, ...)

phi_chat_engine = index.as_chat_engine(llm=phi_lm, ...)

--------------------------------------------------------------------------------------
My code is here, I found change the argument llm doesn't change the chat_engine...

def init_llm(self):
names = [
"llama3.2",
"gemma2",
"mistral",
]

for name in names:
self.lm_dict[name] = Ollama(model=name, request_timeout=120.0)
self.cur_lm = self.lm_dict[names[0]]
self.cur_lm_name = names[0]


def set_current_lm(self, llm_name):

self.cur_lm_name = llm_name
self.cur_lm = self.lm_dict[llm_name]


self.memory = ChatMemoryBuffer.from_defaults(
token_limit=3000,
chat_store=self.chat_store,
chat_store_key=llm_name,
)

self.chat_engine = self.index.as_chat_engine(
chat_mode="context",
llm=self.cur_lm,
memory=self.memory,
system_prompt=(
"You are a helpful assistant which helps users to understand scientific knowledge"
"about biomechanics of injuries to human bodies."
),
verbose=True,
)

Should I create mult index for each language model....?

OK. I figure out. my previous question to the model after switching model is bad, so it generate unclear response...